Book Image

R Deep Learning Cookbook

By : PKS Prakash, Achyutuni Sri Krishna Rao
Book Image

R Deep Learning Cookbook

By: PKS Prakash, Achyutuni Sri Krishna Rao

Overview of this book

Deep Learning is the next big thing. It is a part of machine learning. It's favorable results in applications with huge and complex data is remarkable. Simultaneously, R programming language is very popular amongst the data miners and statisticians. This book will help you to get through the problems that you face during the execution of different tasks and Understand hacks in deep learning, neural networks, and advanced machine learning techniques. It will also take you through complex deep learning algorithms and various deep learning packages and libraries in R. It will be starting with different packages in Deep Learning to neural networks and structures. You will also encounter the applications in text mining and processing along with a comparison between CPU and GPU performance. By the end of the book, you will have a logical understanding of Deep learning and different deep learning packages to have the most appropriate solutions for your problems.
Table of Contents (17 chapters)
Title Page
About the Authors
About the Reviewer
Customer Feedback

Data normalization

Data normalization is a critical step in machine learning to bring data to a similar scale. It is also known as feature scaling and is performed as data preprocessing.


The correct normalization is very critical in neural networks, else it will lead to saturation within the hidden layers, which in turn leads to zero gradient and no learning will be possible.

Getting ready

There are multiple ways to perform normalization:

  • Min-max standardization: The min-max retains the original distribution and scales the feature values between [0, 1], with 0 as the minimum value of the feature and 1 as the maximum value. The standardization is performed as follows:

Here, x' is the normalized value of the feature. The method is sensitive to outliers in the dataset.

  • Decimal scaling: This form of scaling is used where values of different decimal ranges are present. For example, two features with different bounds can be brought to a similar scale using decimal scaling as follows:


  • Z-score...